Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

Supramolecular Zn(II)-Dipicolylamine-Azobenzene-Aminocyclodextrin-ATP Complex: Design and ATP Recognition in Water

Cyclodextrins (CyDs) are water-soluble host molecules possessing a nanosized hydrophobic cavity. In the realm of molecular recognition, this cavity is used not only as a recognition site but also as a reaction medium, where a hydrophobic sensor recognizes a guest molecule. Based on the latter concept, we have designed a novel supramolecular sensing system composed of Zn(II)-dipicolylamine metal complex-based azobenzene (1-Zn) and 3A-amino-3A-deoxy-(2AS,3AS)-γ-cyclodextrin (3-NH₂-γ-CyD) for sensing adenosine-5′-triphosphate (ATP). 1-Zn showed redshifts in the UV-Vis spectra and induced circular dichroism (ICD) only when both ATP and 3-NH₂-γ-CyD were present. Calculations of equilibrium constants indicated that the amino group of 3-NH₂-γ-CyD was involved in the formation of supramolecular 1-Zn/3-NH₂-γ-CyD/ATP. The Job plot of the ICD spectral response revealed that the stoichiometry of 1-Zn/3-NH₂-γ-CyD/ATP was 2:1:1. The pH effect was examined and 1-Zn/3-NH₂-γ-CyD/ATP was most stable in the neutral condition. The NOESY spectrum suggested the localization of 1-Zn in the 3-NH₂-γ-CyD cavity. Based on the obtained results, the metal coordination interaction of 1-Zn and the electrostatic interaction of 3-NH₂-γ-CyD were found to take place for ATP recognition. The “reaction medium approach” enabled us to develop a supramolecular sensing system that undergoes multi-point interactions in water. This study is the first step in the design of a selective sensing system based on a good understanding of supramolecular structures.     

认同度修正下的近相邻改进推荐算法研究

如何融合多方因素准确地为用户提供个性化产品一直是关注的热点问题，由此，一个新的近相邻改进算法融入了大众化认同度和个性化认同度，利于更加高效地挖掘隐藏信息。实验结果表明，相对于传统近相邻算法，认同度修正算法虽然查全率小幅度上下波动，但其他多个评价指标都得到极大提升，假正率和深度有所减少，查准率、[F1]值和提升度得以增加，并且，受试者特征曲线和提升曲线也都说明此修正算法具有更为显著的推荐效果。     

基于改进Faster R-CNN及YOLO的大角度车牌识别算法

随着大数据时代的到来,智慧城市等一系列概念的提出,人工智能开始在城市各个角落得到广泛应用。其中,车牌识别作为城市道路交通重要的一环,也取得了重大突破。基于深度学习的车牌识别一经提出,各类算法应用于车牌检测及识别上,对比于传统的车牌识别,大大提高了识别的速率和准确率。然而,在非限制条件下,如大角度车牌识别准确率仍有提升的空间。提出了一种改进的FasterR-CNN与YOLO的深度学习车牌识别算法,将对大角度下的车牌识别准确率达到了99.7%。由于是轻量型的框架,为后续的移动设备部署工作提供了便利。     

Determination of olaquindox,carbadox and cyadox in animal feeds by ultra-performance liquid chromatography tandem mass spectrometry

Olaquindox, carbadox, and cyadox are chemically synthesised antibacterial and growth-promoting agents for animals. At high doses they may exert mutagenicity and hepatic and adrenal toxicities in animals. Regrettably, these substances are frequently abused or misused when added into animal feeds. Thus, developing a sensitive and reliable method for simultaneous determination of olaquindox, carbadox, and cyadox in different kinds of animal feeds is crucially important for food safety monitoring. In this paper we optimised instrumental conditions, extraction solvents, solid phase extraction cartridges, and pH of the loading solvents on the Oasis HLB cartridge. Under the optimal conditions, mean recoveries ranged from 74.1 to 111%, and intra-day and inter-day variations were lower than 14.6% and 10.8%, respectively. The limits of quantification for olaquindox, carbadox, and cyadox were 0.05 mg kg^?1, 0.10 mg kg^?1, and 0.025 mg kg^?1, respectively. The proposed method uses ultra-performance liquid chromatography tandem mass spectrometry and is sensitive and reliable for the simultaneous determination of olaquindox, carbadox, and cyadox in three kinds of animal feeds (specifically, mixed feed, concentrated feed, and additive premixed feed). This method has good precision, high sensitivity, and good reproducibility, and thus it can be used for convenient and accurate determination of olaquindox, carbadox, and cyadox in different kinds of animal feeds.     

High-roubustness keystroke recognition method based on acoustic spatial gradient

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.     

Characterisation of the Dynamic Interactions between Complex N-Glycans and Human CD22

CD22 (Siglec-2) is a B-cell surface inhibitory protein capable of selectively recognising sialylated glycans, thus dampening autoimmune responses against self-antigens. Here we have characterised the dynamic recognition of complex-type N-glycans by human CD22 by means of orthogonal approaches including NMR spectroscopy, computational methods and biophysical assays. We provide new molecular insights into the binding mode of sialoglycans in complex with h-CD22, highlighting the role of the sialic acid galactose moieties in the recognition process, elucidating the conformational behaviour of complex-type N-glycans bound to Siglec-2 and dissecting the formation of CD22 homo-oligomers on the B-cell surface. Our results could enable the development of additional therapeutics capable of modulating the activity of h-CD22 in autoimmune diseases and malignancies derived from B-cells.     

基于条件信息卷积生成对抗网络的图像识别

传统的图像识别方法需要大量有标签样本进行训练，且模型训练难以达到稳定。针对这些问题，结合条件生成网络和信息最大化生成网络的结构优势建立了条件信息卷积生成网络（C-Info-DCGAN）。模型增加图像的类别信息和潜在信息作为输入数据，然后利用Q网络去更好地发挥类别信息和潜在信息对训练的引导作用，并且利用深度卷积网络来加强对图像特征的提取能力。实验结果表明，该方法能够加快模型训练收敛速度，并有效提高图像识别的准确率。     

面向智能客服系统的情感分析技术

该文以阿里小蜜为例,对智能客服系统中的情感分析技术进行比较全面的介绍,包括情感分析算法模型的原理及其在智能客服系统的多个应用场景中的实际落地使用方式和效果分析。智能客服在解决客户高频业务问题的同时,也需要给客户提供多维度的、具有类人能力的助理、导购、语聊和娱乐等服务,提高客户对智能客服机器人的整体满意度。在此过程中,情感分析技术在机器人类人能力建设中起到了至关重要的作用。该文围绕智能客服系统中人机结合的服务形式,从六个维度总结和介绍了情感分析技术在智能客服系统中的应用场景,包括用户情感检测、用户情感安抚、情感生成式语聊、客服服务质检、会话满意度预估和智能人工入口。     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。